{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 可视化对象的呈现"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 可视化的步骤"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "步骤一：数据准备\n",
    "步骤二：确定图表\n",
    "步骤三：分析迭代\n",
    "步骤四：输出结论"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Matplotlib —— 基本操作"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 常用设置"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "import scipy\n",
    "#包含不同的pdf\n",
    "from scipy import stats\n",
    "#让图片显示在notebook中\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IS/bx9IoKr6OIJCSVLpE49vTKClKSfMyamO91FIkC/dKSmTluMAvWVNHcFvA6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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c3f60b1c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))#先生成一个10*5的图\n",
    "x=np.arange(-5,5,0.01)#生成数字序列\n",
    "y=stats.norm.pdf(x,0,1)#loc=mean,scale=sigma 用统计学包输出值为0，反差为1的正态分布\n",
    "plt.plot(x,y)#画图\n",
    "plt.savefig(\"figure1.png\")# 把图保存为figure1（不支持jpg）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c3f6316c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,5))#关键：重新调用.figure函数会清空画面\n",
    "plt.savefig(\"figure2.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c3f60e7ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.gcf()#调用.gcf()获取上一个图\n",
    "fig.set_size_inches(5,8)\n",
    "plt.savefig(\"figure3.png\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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C0SwpVaW1ZySmL+wOV1eax9i0l3ODE06XYhxiwWiWVNfQJCOTnpi+eUS4ukAHTKudTict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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c3f62fe0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y)\n",
    "fig=plt.gcf()\n",
    "fig.set_size_inches(5,5) #设置图像，类似于指针\n",
    "plt.savefig(\"figure4.png\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 常用图形\n",
    "    曲线图：matplotlib.pyplot.plot(data)\n",
    "    灰度图：matplotlib.pyplot.hist(data)\n",
    "    散点图：matplotlib.pyplot.scatter(data)\n",
    "    箱式图：matplotlib.pyplot.boxplot(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c3f631d588>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c3f64740f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=np.arange(-5,5,0.1)#-5到5区间，相隔0.1取点为list\n",
    "y=x**2\n",
    "plt.plot(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([   6.,   22.,   68.,  174.,  219.,  246.,  176.,   64.,   21.,    4.]),\n",
       " array([ -3.06414566e+00,  -2.45075502e+00,  -1.83736437e+00,\n",
       "         -1.22397373e+00,  -6.10583083e-01,   2.80756206e-03,\n",
       "          6.16198207e-01,   1.22958885e+00,   1.84297950e+00,\n",
       "          2.45637014e+00,   3.06976079e+00]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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q6oJ+qxpOkouBv+P1x3hc33NJK5bkNuA8Fp6Y+CJwXVXd1GtRK5Tk94D/Ap5k4d81wLXd\n3fdrRpLfAXay8PfpHcAdVfVXq3Ls1oJekvRGzU3dSJLeyKCXpMYZ9JLUOINekhpn0EtS4wx6SWqc\nQS9JjTPoJalx/w+plJbIwDKT/wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c3f64c3e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x= np.random.normal(size=(1000))#随机生成正态分布的1000个样本\n",
    "plt.hist(x,bins=10)\n",
    "#灰度图histogram，其实bins是柱子的数目"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1c3f756ce10>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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aqaggFPhvGCd/cgmTezejVCzElo4KNFZ3j9+1FW8dubP5P52oqbJQa0kB2uTp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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c3f62d1518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x= np.random.normal(size=(1000))#随机生成正态分布的1000个样本\n",
    "y= np.random.normal(size=(1000))#随机生成正态分布的1000个样本\n",
    "#散点图\n",
    "plt.scatter(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'boxes': [<matplotlib.lines.Line2D at 0x1c3f75d5748>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x1c3f75e02b0>,\n",
       "  <matplotlib.lines.Line2D at 0x1c3f75e0710>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x1c3f75e0fd0>],\n",
       " 'means': [],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x1c3f75e0b70>],\n",
       " 'whiskers': [<matplotlib.lines.Line2D at 0x1c3f75d5978>,\n",
       "  <matplotlib.lines.Line2D at 0x1c3f75d5e10>]}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXYAAAD8CAYAAABjAo9vAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c3f75625c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 箱式图  （正态分布）\n",
    "plt.boxplot(x)       "
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "常用样式\n",
    "颜色/线型/标记\n",
    "    pyplot.plot(x, color='g') 颜色\n",
    "    pyplot.plot(x, linestyle='—') 线形\n",
    "    pyplot.plot(x, marker='+') 曲线上的点\n",
    "    pyplot.plot(x, 'ro-')\n",
    "标题\n",
    "    pyplot.title(\"xxxx\")\n",
    "坐标轴标签\n",
    "    pyplot.xlabel(\"xxxx\")\n",
    "    pyplot.ylabel(\"xxxx\")\n",
    "图注\n",
    "    pyplot.legend(loc=\"best\")\n",
    "配合\n",
    "    pyplot.plot(label=\"xxxx\")\n",
    "网格\n",
    "    pyplot.grid(color=\"xxx\", linestyle=\"-\", alpha=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c3f7637f60>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IDttxlAu8/8P7dJndhXrl6/HtY9/ajqPcQAtd/UFCXAI31r6RZY8sIz0rneum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\nulJK+QktdKWU8hNa6Eop5Sf+D/INIEHTFU3hAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c3f75f11d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(-5,5.5,0.5)\n",
    "y = x ** 2\n",
    "# 颜色/线型/标记\n",
    "plt.plot(x,y,color='g',linestyle='--',marker='+')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c3f769a550>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1at1jO3eG88+HXXbxMe8PPvCx/F12gVtu8XnzLTHTVnCSU4WYtrgD8GGT+x8B\nB7Xj3xNpvX79Mu/e06cP3H23F+T99/djixb5IqZNtfJdtQquvdY/b1zBuXixF/Qjj4T77lv3C+Gg\ngzJPM9RMFYmkPQXdMhzb6FTJzEYAIwD66YUuuTZhwsbj2OXl8OtfwxFHrP/YAQN8hsznn/vinb33\nznx2b+Y9Zvr2XX/5/a67+q3Rtddmfm7NVJFI2jNt8SOgb5P7OwIfb/igEMKUEEJVCKGqZ8+e7Xg6\nkQwy9WFv6YLollv6RcvmTjD69fOz8JZ6qbTluUXyqD1j6B2B/wccDiwEXgOGhRCaXQGiMXQpKplm\nqaS9h4wkUt5nuYQQ1gCjgf8DzAYe3lQxFyk6OsOWlFFzLhGRIpf3M3QRESkuKugiIimhgi4ikhIq\n6CIiKaGCLiKSEgWd5WJmdUCGddpFrweQYY+01Cq1rxf0NZeKpH7NFSGEFldmFrSgJ5WZ1WYzZSgt\nSu3rBX3NpSLtX7OGXEREUkIFXUQkJVTQszMldoACK7WvF/Q1l4pUf80aQxcRSQmdoYuIpIQKeiuY\n2TgzC2bWI3aWfDOz35jZu2b2lpk9ZmbdYmfKFzM72szmmNlcM7s4dp58M7O+Zvacmc02s7fNbEzs\nTIVgZmVm9oaZ/TF2lnxRQc+SmfUFjgQy7DmWSk8Dg0IIe+N97y+JnCcvSnSz8zXAhSGE/sDBwHkl\n8DUDjMFbfaeWCnr2bgR+ToZt9tIohPCXhp73AP8X35EqjQ4E5oYQ3g8hfAU8CJwQOVNehRAWhRBe\nb/h8BV7kdoibKr/MbEfgWOC22FnySQU9C2Z2PLAwhPBm7CyR/Aj4U+wQeZJps/NUF7emzKwS2A94\nJW6SvPtP/ISsPnaQfGrPJtGpYmbPAL0y/NFlwKXAUYVNlH+b+ppDCI83POYy/L/oNYXMVkBZbXae\nRma2JTANGBtCWB47T76Y2XHAkhDCDDMbEjtPPqmgNwghHJHpuJntBewEvGlm4EMPr5vZgSGEfxYw\nYs419zU3MrMzgeOAw0N657dmtdl52phZJ7yY14QQfh87T54NBo43s2OALYCtzOy+EMLpkXPlnOah\nt5KZzQOqQghJbPCTNTM7GrgBODSEUBc7T760ZbPzpDM/M7kbWBZCGBs7TyE1nKGPCyEcFztLPmgM\nXZpzE9AVeNrMZprZ5NiB8qFENzsfDJwBfLvhZzuz4exVEk5n6CIiKaEzdBGRlFBBFxFJCRV0EZGU\nUEEXEUkJFXQRkZRQQRcRSQkVdBGRlFBBFxFJif8PktBMLTWUX8MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c3f75f9630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y,'ro--') # 可以合在一起写 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c3f764bb00>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5W2gZHspkP7/mwNFYEZyAC/t2ICW+JU/Pt0NJjQlm+fvLeXv5dq5ITyYuqoXrOCfMiuAE\nhIYIN4zsQmb2XjKz7bQTxgSrGQu3UVOrXH9GF9dRTooVwQm6PD2F1i3DefqLra6jGGMcOFBRzcxF\n2Yzp14G0ttGu45wUK4ITFB0Rxo+Gp/Lh2l1s213qOo4xxsdmLc1lf3nzO51EQ6wITsI1IzoTHhLC\n9AW2VWBMMKmuqWX6gq0M6dyGQaltXMc5aVYEJ6Fdq0jGD0ri1cxc9pRWuo5jjPGRD1bvYvu+gwGx\nNQBWBCftx6O6Ul5Vywt22gljgoKqMu2LLXRJiOb83u1dx2kSVgQnqUf7WM7pmchzC7dRXmWnnTAm\n0C3euodV24v58aguzfJ0Eg2xImgCU87sSlFpJW9+vd11FGOMlz39xRbio1vww8HJrqM0GSuCJjCi\na1v6dWrF0/O3UGunnTAmYGUVlPDJ+gKuHpFGZHjzPJ1EQ6wImoCIMGVUV7YUlvLp+gLXcYwxXvLM\n/K1EhIVw1fA011GalBVBE7no1I50imvJNDvthDEBqaCknDeWbeey05JpGxPhOk6TsiJoIuGhIVw3\nsjNLtu5hee4+13GMMU3s+a+yqaqt5YZmfjqJhlgRNKErh6YSGxlmJ6MzJsCUVVbz/KJsRvduT9fE\nGNdxmpwVQROKiQhj0rBUPli1k9w9Za7jGGOayGuZeewrq2qW1yNuDCuCJnbd6V0IEbHTThgTIGpq\nlWfmb2VQahynpTX/00k0xIqgiXVoHcklA5OYlZFLcVmV6zjGmJM0d+0ucvaUMXVUV0QCYwLZ4awI\nvGDKqK6UVdbw7Je2VWBMc6aqPDYvi9T4KC7o28F1HK+xIvCC3h1bcWHf9jy7YCv7yuxkdMY0Vx+u\nyWf19v387NxTCA2Q00k0xIrAS34xugcHKquZ9oUdQWRMc1RbqzwydyNdE6K5dFAn13G8yorAS3p1\naMXY/knMWLiN3QcqXMcxxhyn91btZEN+Cbec352w0MD+qAzsd+fYred3p7yqhqc+2+w6ijHmOFTX\n1PLIxxvp0T6Gi/snuY7jdV4rAhF5VkQKRGR1vcfuEZHtIrLcc7vIW+v3B90SY7h0UDLPL8omf3+5\n6zjGmEZ6e/kOthSWctvoHgFzqumj8eYWwQxgTAOPP6KqAz239724fr9wy3ndqalVnpiX5TqKMaYR\nqmpq+ccnm+ib1IoLA/hIofq8VgSq+gWwx1u/v7lIbRvF5enJvLQkl+37DrqOY4w5htcy88jZU8Zt\no3sE7LyBw7kYI7hZRFZ6dh0dcZqeiEwVkQwRySgsLPRlviZ387ndAXjs002OkxhjjqaiuoZ/fbKJ\ngSlxnNurnes4PuPrIngS6AYMBHYCDx1pQVWdpqrpqpqemJjoq3xe0SmuJROHpjArI4/solLXcYwx\nR/Dyklx2FJdz+wXBszUAPi4CVc1X1RpVrQWeBob6cv0u3XTOKYSFCP/4xLYKjPFH5VU1PD4vi6Fd\n4jnjlATXcXzqmEUgdX4kInd57qeKyAl9gItIx3p3LwVWH2nZQNOuVSRXj0jjra+3k1VwwHUcY8xh\nXliUTUFJBbcH0djAIY3ZIngCGAFM9NwvAR4/1otE5CXgK6CniOSJyA3AgyKySkRWAucAvzix2M3T\njWd1IzI8lEc/3ug6ijGmntKKap74bDNnnJLAsK5tXcfxubBGLDNMVQeLyNcAqrpXRFoc60WqOrGB\nh6cfb8BA0jYmgutGdubxeZu56Zz99O7YynUkYwwwY+E29pRWctsFPVxHcaIxWwRVIhIKKICIJAK1\nXk0VwKaM6kpsRBiPzLWtAmP8wf7yKqZ9sYVze7VjcGpgXm/gWBpTBP8E3gTaicifgAXAn72aKoDF\nRbXghlFd+GhtPqvyil3HMSboTZ+/leKDVdw2Oji3BqARRaCqM4FfA3+h7pDP8ar6qreDBbLrz+hC\nXFQ4D8/d4DqKMUFtb2klzy7Yypi+HejXqbXrOM405qiheKAAeAl4EcgXkXBvBwtkrSLDmXpmV+Zt\nKCQze6/rOMYErWnzt3CgsppfBPHWADRu19AyoBDYCGzy/LxVRJaJyGneDBfIrj29MwkxLWyrwBhH\ndh+oYMaX27i4fxI9O8S6juNUY4pgDnCRqiaoalvge8As4P+oO7TUnICoFmHceFY3vswq4qvNRa7j\nGBN0nvpsMxXVNdxyfnfXUZxrTBGkq+qHh+6o6kfAmaq6CIjwWrIg8KPhabRvFcHDczegqq7jGBM0\n8veX8/yibC4dlEy3xBjXcZxrTBHsEZHfiEia5/ZrYK/nkFI7jPQkRIaHcvM5p7B0217mb9rtOo4x\nQePxeVnU1Cq3nGdbA9C4IpgEJANvAW8DqZ7HQoErvBctOFwxJIVOcS156CPbKjDGF/L2lvHSkhwu\nT08htW2U6zh+oTGHj+5W1Z+p6iDPxWRuVtVCVa1UVbvaykmKCAvlZ+eewoq8Yj5ZV+A6jjEB77FP\nsxCEn517iusofqMxh4/2EJFpIvKRiHx66OaLcMHih6clk9Y2iofnbqS21rYKjPGWbbtLeTUzj0nD\nUkmKa+k6jt9ozLmGXgWeAp4BarwbJziFh4Zwy3nduW3WCuas2cVFp3Y89ouMMcftn59sIixE+L+z\nu7mO4lcaM0ZQrapPquoSVc08dPN6siAzbmAnuiVG88jcjdTYVoExTS6r4ABvLd/O1SPSaNcq0nUc\nv9KYInhHRP5PRDqKSPyhm9eTBZnQEOEXo3uwqeAA767c4TqOMQHn0Y83Ehkeyo1n2dbA4RpTBNcA\nvwIWApmeW4Y3QwWri/p1pFeHWB79eBPVNXZkrjFNZd3O/by7cifXjexM2xib/nS4xhw11KWBW1df\nhAs2ISHCbaN7sHV3Ka8vy3Mdx5iA8fDcjcRGhjF1lG0NNKQxg8WISD+gD/DNjjVV/a+3QgWz0X3a\nMzAljr99uJGLTu1IbKSd38+Yk/Fl1m7mrs3nlxf0oHWU/XtqSGMOH70b+Jfndg7wIHCJl3MFLRHh\n3kv6UlRawT/tQvfGnJSqmlrunr2G1PgofjzKdmQcSWPGCC4DzgN2qep1wADsHENeNSAljgnpKfzn\ny21syi9xHceYZuu5hdvIKjjAXWP7EBke6jqO32pMERxU1VqgWkRaUXdtAqtWL/vVhT2JahHKPe+s\nsVNPGHMCCvaX8+jHmzinZyLn9W7nOo5fa0wRZIhIHPA0dUcMLQOWeDWVoW1MBLdf0JMvs4qYs3qX\n6zjGNDsPzFlPZXUtd13cFxFxHcevHbUIpO5v7y+quk9VnwJGA9d4dhEZL5s8LJVeHWK5/711HKy0\nSd3GNFZm9h7eWLadH4/qQpeEaNdx/N5Ri0Dr9km8Ve/+NlVd6fVUBoCw0BDuvaQv2/cd5MnP7Px+\nxjRGTa1y19tr6NAqkpvOsRPLNUZjdg0tEpEhXk9iGjSsa1vGDUziqS+2kF1U6jqOMX7vpSU5rNmx\nn999vzfREY06Qj7oNaYIzgG+EpHNIrJSRFaJiG0V+NCdF/UmPET447trXUcxxq/tLa3k7x9tYHjX\neMb2t5M3NlZj6vJ7Xk9hjqp9q0h+dl53HvhgPfPWF3BOLzsCwpiG/P2jDZSUV3PvJf1sgPg4NOYU\nE9kN3XwRzvzP9SO70DUhmnvfWUNFtQ0cG3O41duLeXFJDlePSKNnh1jXcZqVxuwaMn6gRVgI91zS\nl21FZUxfsNV1HGP8Sm2tctfbq2kb3YJbz+/hOk6zY0XQjJzZI5EL+rTnX59ksbP4oOs4xviNN7/e\nzrKcffx6TC9at7TzCR2vxpxr6GYRaeOLMObY/jC2D7Wq/Om9da6jGOMX9pdX8ZcP1jMwJY7LBie7\njtMsNWaLoAOwVERmicgYsREYp1Lio7jxrG68u3InX20uch3HGOf++fEmikoruG9cX0JC7OPpRDRm\nsPj3QHdgOnAtsElE/iwiRz2xt4g8KyIFIrK63mPxIjJXRDZ5/rQtjRPw07O7kdymJffMXmMXsDFB\nbVN+CTMWbuPKISn0T45zHafZatQYgWeG8S7PrRpoA7wmIg8e5WUzgDGHPfZb4BNV7Q584rlvjlNk\neCh/GNuHDfklPL/IDuAywUlVueedNURHhPGrC3u5jtOsNWaM4OcikknddQi+BE5V1Z8CpwE/PNLr\nVPULYM9hD48DnvP8/Bww/kRCG7igT3tGdU/g4Y82UlhS4TqOMT73wepdfJlVxO0X9CA+uoXrOM1a\nY7YIEoAfqOqFqvqqqlYBeE5NPfY419deVXd6Xr8TOOLMKBGZKiIZIpJRWFh4nKsJfCLCPZf0pby6\nhgfnrHcdxxifKqus5v5319K7YysmDU11HafZa8wYwV1HmkCmql47dEVVp6lquqqmJyYmems1zVq3\nxBiuP6MLr2bm8XXOXtdxjPGZJz/bzI7icu4b15ewUDsK/mT5+m8wX0Q6Anj+LPDx+gPOz87tTrvY\nCO6evYbaWruAjQl82UWl/PvzLYwfmMSQzvGu4wQEXxfBbOAaz8/XAG/7eP0BJyYijN99vzcr84qZ\nlZHrOo4xXvfHd9cSHirccVFv11EChteKQEReAr4CeopInojcADwAjBaRTdRd5OYBb60/mFwyIImh\nneP565z17CurdB3HGK/5dH0+H68r4Ofndad9q0jXcQKG14pAVSeqakdVDVfVZFWdrqpFqnqeqnb3\n/Hn4UUXmBBwaOC4+WMXDcze6jmOMV1RU13DfO2vpmhjNdSO7uI4TUGyUJUD0SWrFVcPTeGFRNmt3\n7Hcdx5gm98z8rWwrKuOei/vSIsw+upqS/W0GkNtG9yQuqgW/f2sVNTZwbAJITlEZj32axYV923Nm\nDzuKsKlZEQSQ1lHh3DW2D8ty9vHU55tdxzGmSVTX1PKLWcsJCxXuvriv6zgByYogwIwbmMTY/h15\nZO5GVubtcx3HmJP25Gebyczey/3j+5EU19J1nIBkRRBgRIQ/jT+VhJgIbn1lOQcr7WpmpvlambeP\nf3yyiYsHJDFuYCfXcQKWFUEAah0VzkNXDGBLYSl/+cCuW2Cap4OVNdz6ynISYyO4f1w/13ECmhVB\ngBp5SgI3nNGF/36VzWcbbAK3aX7+/P46thSW8tDlA2gdZVcd8yYrggD2qwt70rN9LL96bSV7Sm2i\nmWk+5m0o4PlF2fz4jC6cfkqC6zgBz4oggEWGh/LIhIEUl1Vx5xurqLushDH+bU9pJb9+bSW9OsTy\nywt7uo4TFKwIAlyfpFbcfkEP5qzZxWuZea7jGHNUqspvX19JcVkVj0wYSGR4qOtIQcGKIAj8eFRX\nhnWJ557Za8gpKnMdx5gjejUjj4/W5vPLC3vQu2Mr13GChhVBEAgNER66YgAhItw2a7nNOjZ+Kaeo\njHvfWcPwrvH8+IyuruMEFSuCIJHcJor7xvclI3uvzTo2fufQ7OGQEOGhKwYSEiKuIwUVK4IgMn5g\np29mHa/KK3Ydx5hvPPX5/2YPd7LZwz5nRRBEvj3r+GubdWz8wsq8fTz6sc0edsmKIMgcmnW8ubCU\nB2zWsXHMZg/7ByuCIDTylASuH9mF52zWsXHsLx/UzR7+u80edsqKIEj9ekxPerSPsVnHxpl5Gwr4\n71fZ3HBGF0ba7GGnrAiCVGR4KI9OGMS+skqbdWx87tDs4Z7tY/mVzR52zoogiNXNOu5ps46NT6kq\nd7xhs4f9iRVBkJvimXV87ztryd1js46N972amceHa+pmD/dJstnD/sCKIMgdmnUswC9esVnHxrty\nisq4d7bNHvY3VgTGZh0bn6iuqeU2mz3sl6wIDPC/WccPz93Igk27XccxAeiBD9aTkb2XP46z2cP+\nxorAAHWzjv/yg1Pp3i6Gn87MZFN+ietIJoC8sCibZxZs5drTOzN+kM0e9jdWBOYbsZHhTL92CJHh\noVw3YymFJRWuI5kA8PnGQu6evYZze7XjD2P7uI5jGmBFYL6lU1xLpl+Tzu4DFUz5bwblVXY+InPi\n1u/az00zl9GjfSz/nDiIUBsX8EtWBOY7+ifH8eiEQazI28fts1ZQa0cSmRNQUFLODTMyiI4I5dlr\n04mJCHMdyRyBFYFp0Jh+Hbjje714b9VO/v7RBtdxTDNzsLKGKc9lsKe0kunXDKFjaxsc9mdW0eaI\npozqytbdZTzx2WY6J0RzRXqK60imGaitVX7xynJWbi9m2lXp9OvU2nUkcwxOikBEtgElQA1Qrarp\nLnKYoxMR7hvXl7y9Zdz5xio+2CMdAAAPI0lEQVSS41pyup0czBzDX+esZ86aXfxhbB9G92nvOo5p\nBJe7hs5R1YFWAv4tPDSExycPpmtiNDe+kElWwQHXkYwfe2lJDv/+YgtXDU/j+pGdXccxjWRjBOaY\nWkWGM/2aIbQIC+G6GUsoOmCHlZrvmr+pkN+/tZqzeyZy98V9ELEjhJoLV0WgwEcikikiUxtaQESm\nikiGiGQUFhb6OJ45XEp8FE9fnU7B/gqmPp9ph5Wab9mYX8L/vbCM7u1i+NfEQYSF2nfM5sTVf62R\nqjoY+B5wk4icefgCqjpNVdNVNT0xMdH3Cc13DEptwyMTBpKZvZdfvbbSDis1ABSWVHDdf5YS2SKU\n6dcOITbSrjTW3DgpAlXd4fmzAHgTGOoihzl+F53akd+M6cU7K3bwyMcbXccxjpVX1TDlvxkUlVYw\n/Zp0O4dQM+XzIhCRaBGJPfQzcAGw2tc5zIm78ayuTEhP4V+fZtkFbYJYba1y26zlrMjbx6MTBtE/\nOc51JHOCXBw+2h540zOQFAa8qKpzHOQwJ0hEuP/SfuTuLeOON1bSKa4lI7q1dR3L+NjfPtrA+6t2\ncedFvRjTr4PrOOYk+HyLQFW3qOoAz62vqv7J1xnMyQsPDeHJyaeRGh/FjS9ksrnQDisNJrOW5vLk\nZ5uZODSVKaPsAjPNnQ3tmxPWOiqc/1w7lLAQ4foZS9lTWuk6kvGBL7N2c+ebqxjVPYH7xvW1w0QD\ngBWBOSmpbaOYdnU6O4vL+cnzdrbSQLcpv4QbX8ika2I0j08eTLgdJhoQ7L+iOWmnpbXhocsHkJG9\nl6umL6a4rMp1JOMFy3P3ccW/vyIiLJTp1wyhlR0mGjCsCEyTuHhAEv+aOOibD4v8/eWuI5km9MXG\nQiY9vYjYyHBeu3EEKfFRriOZJmRFYJrM2P5JzLhuKHl7y/jBEwttADlAvL18O9fPWEpa22he++kI\nOidEu45kmpgVgWlSI09J4OWpIyivquHyp75iRe4+15HMSXh2wVZueXk56Z3b8MpPhtMuNtJ1JOMF\nVgSmyZ2a3JrXfno60RGhTHx6EV9stHNFNTeqyoNz1nPfu2sZ07cDM64bamMCAcyKwHhFl4RoXr/x\ndNLaRnPDc0t5e/l215FMI1XX1PKb11fyxGebmTQslccnDyYyPNR1LONFVgTGa9q1iuSVnwxncGob\nbnl5Of/5cqvrSOYYyqtquPGFZczKyOPn53XnT+P72QXng4AVgfGqVpHhPHf9UMb07cC976zlbx+u\nR9XOWuqPisuquGr6Yj5Zn88fx/XlttE9bLJYkLAiMF4XGR7K45MHM3FoKo/P28xvX19FdU2t61im\nnvz95Vzx769YkVvMYxMHc9WIzq4jGR+yi9cbnwgNEf58aT8SY1rwz0+zKCqt5LFJg2zfsx/YXHiA\nq6cvofhgFTOuG2LXpQ5CtkVgfEZEuO2Cntw3ri+frM+3Wch+YEXuPi5/6isqqmt4eepwK4EgZUVg\nfO7qEZ1tFrIf+GJjIROfXkRMRBiv3Xg6/Tq1dh3JOGJFYJw4fBbyFpuF7FNvL9/ODc/ZbGFTx4rA\nOFN/FvJlT33Fws27XUcKeDW1yhOfZXHLy8s5Lc1mC5s6VgTGqUOzkFu3DGfS04u5441V7C+3cQNv\n2LCrhB8+uZAH52zg+/072mxh8w07asg41yUhmvd/PopHPt7IM/O3MG99AfeP78f5fdq7jhYQKqtr\neeKzLB6fl0VsZDj/nDiIi/t3tDkC5hvSHCb3pKena0ZGhusYxgdW5O7jN6+vZP2uEi4ZkMTdF/eh\nbUyE61jN1orcffz6tZVsyC9h3MAk7r64L/HRLVzHMj4iIpmqmn6s5WyLwPiVASlxzL75DJ78bDOP\nzdvE/E2F3HNJXy4ZkGTfYI/DwcoaHp67gekLttIuNpLp16RzXm/bwjINsy0C47c25pfw69dWsjx3\nH+f1asf9l/ajY+uWrmP5vYWbd3PHG6vILipj0rBUfvu9XjYWEKQau0VgRWD8Wk2tMmPhNv7+4QZC\nQ4Q7LurFxCGphNiJ0L5jf3kVf3l/PS8tyaFz2yj+8oP+jOjW1nUs45AVgQkoOUVl3PHmSr7MKmJ4\n13ge+EF/O/a9no/X5vO7t1ZRWFLBlFFdufX8HrRsYafvCHZWBCbgqCqzMnK5/711VFbXcvsFPbh+\nZBfCQoP3KOiiAxXc+85aZq/YQa8OsTx4WX/6J8e5jmX8hA0Wm4AjIkwYksrZPdvx+7dW8+f31/Pe\nyp389bL+9OrQynU8n1JVZq/YwT2z13CgoprbRvfgxrO60SIseEvRnDjbIjDNkqry3qqd3P32GooP\nVjFpWCoTh6bSu2NgF0J1TS2fbyxkxsJtzN+0m4EpcTx4WX96tI91Hc34Ids1ZILC3tJKHvhgPW9+\nvZ3KmloGJLdmwpBULh7QkdgAOlImp6iMWRm5vJqZS/7+ChJiIvjp2d249vTOdgUxc0RWBCao7C2t\n5K3l23l5SS4b8ktoGR7K9/t35MohKZyW1qZZzkEor6rhwzW7eGVpLgs3FxEicHbPdkwYksK5vdoR\nHsRjI6ZxrAhMUFJVVuQV88rSHGYv30FpZQ3dEqOZMCSFHwxOJqEZzFJet3M/ryzN5c2vt1N8sIqU\n+JZMSE/hstNS6NDaThBnGs+KwAS90opq3lu1k1eW5pKZvZewEGF0n/ZMGJLCqO6JfrVLpaS8indW\n7OSVpTmsyCumRWgIY/p1YMKQFEZ0bWvzJswJsSIwpp6sghJeWZrL68u2s6e0kqTWkVyWnsLlpyWT\nEh/lJJOqkpm9l5eX5vLeyp0crKqhV4dYJgxJYfzATrSxcwKZk+TXRSAiY4B/AKHAM6r6wNGWtyIw\nTaWyupaP1+Xz8tJc5m8qBKB/chyd20aRGh9FSpsokuNbktImio6tI5tkjsLByhry9paRu7eM3D0H\nyd1TRs6eMjbkl5BdVEZ0i1AuGdiJK4ek0D+5dbMczzD+yW+LQERCgY3AaCAPWApMVNW1R3qNFYHx\nhu37DvJqRi6LthSRu+cgO4sPUlvvn0NYiJAU15KU+JakxkeR3CaKlPgoUtrU3Y+PboGIUFOr7Cw+\nSM6eMvL2HPR84Nd92OfuPUhhScW31hsZHkJymyjS4qO4sF8Hvn9qR6IjbEqPaXr+PKFsKJClqlsA\nRORlYBxwxCIwxhs6xbXk1vN7fHO/qqaWnfvKyd3r+RD3fJDn7injozX5FJVWfuv1US1CaRPVgvz9\n5VTXa5AQgY6t68rinJ6JpBwqkPgoUuJbkhgTYd/6jV9xUQSdgNx69/OAYYcvJCJTgakAqampvklm\nglp4aAipbaNIbRvFyAaeL62oJm/vwXrf9svYV1ZFx9aRpMT/b9dSx7hIO7TTNCsuiqChr0Lf2T+l\nqtOAaVC3a8jboYw5luiIMHp2iKVnB5vFawKLi68teUBKvfvJwA4HOYwxxuCmCJYC3UWki4i0AK4E\nZjvIYYwxBge7hlS1WkRuBj6k7vDRZ1V1ja9zGGOMqePkmDVVfR9438W6jTHGfJsd2mCMMUHOisAY\nY4KcFYExxgQ5KwJjjAlyzeLsoyJSCGS7znECEoDdrkP4ULC9X7D3HCya63tOU9XEYy3ULIqguRKR\njMac8ClQBNv7BXvPwSLQ37PtGjLGmCBnRWCMMUHOisC7prkO4GPB9n7B3nOwCOj3bGMExhgT5GyL\nwBhjgpwVgTHGBDkrAh8QkV+KiIpIguss3iYifxOR9SKyUkTeFJE415m8RUTGiMgGEckSkd+6zuNt\nIpIiIvNEZJ2IrBGRW1xn8gURCRWRr0XkXddZvMWKwMtEJAUYDeS4zuIjc4F+qtof2Ajc4TiPV4hI\nKPA48D2gDzBRRPq4TeV11cDtqtobGA7cFATvGeAWYJ3rEN5kReB9jwC/poHLcQYiVf1IVas9dxdR\ndwW6QDQUyFLVLapaCbwMjHOcyatUdaeqLvP8XELdh2Mnt6m8S0SSge8Dz7jO4k1WBF4kIpcA21V1\nhessjlwPfOA6hJd0AnLr3c8jwD8U6xORzsAgYLHbJF73KHVf5GpdB/EmJxemCSQi8jHQoYGnfgfc\nCVzg20Ted7T3rKpve5b5HXW7Emb6MpsPSQOPBcVWn4jEAK8Dt6rqftd5vEVExgIFqpopIme7zuNN\nVgQnSVXPb+hxETkV6AKsEBGo20WyTESGquouH0Zsckd6z4eIyDXAWOA8DdyJKnlASr37ycAOR1l8\nRkTCqSuBmar6hus8XjYSuERELgIigVYi8oKq/shxriZnE8p8RES2Aemq2hzPYNhoIjIGeBg4S1UL\nXefxFhEJo24w/DxgO7AUmBTI19+Wum80zwF7VPVW13l8ybNF8EtVHes6izfYGIFpao8BscBcEVku\nIk+5DuQNngHxm4EPqRs0nRXIJeAxErgKONfz33a559uyaeZsi8AYY4KcbREYY0yQsyIwxpggZ0Vg\njDFBzorAGGOCnBWBMcYEOSsCY4wJclYExhyD1LF/KyZg2f/cJuiIyBDP9RIiRSTac279foct09lz\n3v0ngGVAiog8KSIZnuXvrbfsNhG5V0SWicgqEenleTxRROZ6Hv+3iGQfuiaFiPxIRJZ4JmX923Na\na2OcsCIwQUdVlwKzgfuBB4EXVHV1A4v2BP6rqoNUNZu6k+qlA/2Bs0Skf71ld6vqYOBJ4Jeex+4G\nPvU8/iaQCiAivYEJwEhVHQjUAJOb+n0a01h20jkTrO6j7vxA5cDPj7BMtqouqnf/ChGZSt2/m47U\nXZBmpee5QydgywR+4Pn5DOBSAFWdIyJ7PY+fB5wGLPWckLAlUHCyb8iYE2VFYIJVPBADhFN3ZsnS\nBpb55jER6ULdN/0hqrpXRGZ4XndIhefPGv7376qhU1Ufevw5VQ3Iq7eZ5sd2DZlgNQ34A3XXS/hr\nI5ZvRV0xFItIe+ouUXksC4ArAETkAqCN5/FPgMtEpJ3nuXgRSTu++MY0HdsiMEFHRK4GqlX1Rc8g\n7UIROVdVPz3Sa1R1hYh8DawBtgBfNmJV9wIvicgE4HNgJ1CiqrtF5PfAR56jkaqAm4Dsk3tnxpwY\nO/uoMV4iIhFAjapWi8gI4EnP4LAxfsW2CIzxnlRgludbfyUwxXEeYxpkWwTGGBPkbLDYGGOCnBWB\nMcYEOSsCY4wJclYExhgT5KwIjDEmyP0/yu4Ieub3aDwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c3f7660ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"style config\")   # 标题\n",
    "plt.xlabel(\"x range\")       # 横坐标\n",
    "plt.ylabel(\"y range\")       # 纵坐标\n",
    "plt.plot(x,y)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1c3f77588d0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JNy3CL0lZsgSuuw5uvDGsLXXDDXDeeWEZERHJDQoQ2SDu8OSTcNFF4bxQJ54I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\nJEUBIiIiSfn/wYkTKmQt3P8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c3f76c6898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y1 = x ** 2\n",
    "y2 = x ** 3\n",
    "plt.plot(x,y1,'r-',label='line 1')\n",
    "plt.plot(x,y2,'b-',label='line 2')\n",
    "plt.title(\"style config\")\n",
    "plt.xlabel(\"x range\")\n",
    "plt.ylabel(\"y range\")\n",
    "plt.legend(loc=\"best\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JrpPMdgjnzLt3w7XXQk6OOVHs1VchLq7tzxvOmdtKZh91UFpbtkMjlGS2Q7hm/te/zARy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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c3fa07cac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y1,'r')\n",
    "plt.plot(x,y2,'b')\n",
    "plt.grid(color='k',linestyle='--',alpha=0.5)   # 设置网格 alpha参数表示透明度(%)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Matplotlib 练习题"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib as mpl\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 练习1：航班乘客变化分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>passengers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1949</td>\n",
       "      <td>January</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1949</td>\n",
       "      <td>February</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1949</td>\n",
       "      <td>March</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1949</td>\n",
       "      <td>April</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1949</td>\n",
       "      <td>May</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year     month  passengers\n",
       "0  1949   January         112\n",
       "1  1949  February         118\n",
       "2  1949     March         132\n",
       "3  1949     April         129\n",
       "4  1949       May         121"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#获取数据\n",
    "data = sns.load_dataset(\"flights\")\n",
    "data.head()\n",
    "# 年份，月份，乘客数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Annual Variation Trend of Passengers')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JLp0uIiLFFBwxmtSvxehhfVJdhohIuVYt1QWIiEjFouAQEZGEKDhE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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c3f899ad68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#分析年度乘客总量变化情况\n",
    "year_group=data.groupby('year').sum()\n",
    "fig,ax=plt.subplots()\n",
    "ax.plot(year_group.index,year_group['passengers'])\n",
    "ax.set_xlabel('year')\n",
    "ax.set_ylabel('passengers')\n",
    "ax.set_title('Annual Variation Trend of Passengers')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Monthly Distribution of Passengers')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qceAwM7NKHDjMzKwSBw4zM6vEgcPMzCpx4DAzs0ocOMzMrBIHDjMzq2SVCRyS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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c3f64a1da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#分析乘客在一年中各月份的分布\n",
    "#data_1949=data[data['year']==1949]\n",
    "month_group=data.groupby('month').sum()\n",
    "month_group['month_num']=range(12)\n",
    "fig1,ax1=plt.subplots()#\n",
    "ax1.bar(month_group['month_num'],month_group['passengers'],align='center')\n",
    "ax1.set_xlabel('month')\n",
    "ax1.set_ylabel('passengers')\n",
    "ax1.set_xticks(range(12))\n",
    "month_names=[str[:3] for str in list(month_group.index)]\n",
    "ax1.set_xticklabels(month_names)\n",
    "ax1.set_title('Monthly Distribution of Passengers')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 练习2：鸢尾花花型尺寸分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width species\n",
       "0           5.1          3.5           1.4          0.2  setosa\n",
       "1           4.9          3.0           1.4          0.2  setosa\n",
       "2           4.7          3.2           1.3          0.2  setosa\n",
       "3           4.6          3.1           1.5          0.2  setosa\n",
       "4           5.0          3.6           1.4          0.2  setosa"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = sns.load_dataset(\"iris\")\n",
    "data.head()\n",
    "# 萼片长度，萼片宽度，花瓣长度，花瓣宽度，种类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#尺寸为长乘以宽\n",
    "data['sepal_size']=data['sepal_length']*data['sepal_width']\n",
    "data['petal_size']=data['petal_length']*data['petal_width']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'size of petal')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FJd3xzHGSkqf76JnANXEHJc0GZgNMmzYtK5mcJilCA50VceYuI+jV13v/Sc09RZ0fcdqX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\nFV03AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c3f899a048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#花瓣与萼片的关系\n",
    "fig, ax2_1 = plt.subplots()\n",
    "ax2_1.scatter(data['sepal_size'],data['petal_size'])\n",
    "\n",
    "# 添加标题和坐标说明\n",
    "ax2_1.set_title('Size of Sepal vs Size of Petal')\n",
    "ax2_1.set_xlabel('size of sepal')\n",
    "ax2_1.set_ylabel('size of petal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'], dtype=object)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "species=data['species'].unique()\n",
    "species"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data1=data[data['species']==species[0]]\n",
    "data2=data[data['species']==species[1]]\n",
    "data3=data[data['species']==species[2]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'size of petal')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NkKGhjKquFJG9VfXNmOTqgg0NpYvcvW/BMZpGPV6eVL5x09TkPQvLht6MYil7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\n/wOcdiYa/n9DLwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c3f8b115c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#不同种类之间萼片与花瓣的关系\n",
    "fig, ax2_2 = plt.subplots()\n",
    "\n",
    "ax2_2.scatter(data1['sepal_size'],data1['petal_size'],color = '#ff0000',label=species[0])\n",
    "ax2_2.scatter(data2['sepal_size'],data2['petal_size'],color = '#00ff00',label =species[1])\n",
    "ax2_2.scatter(data3['sepal_size'],data3['petal_size'],color = '#0000ff',label=species[2])\n",
    "ax2_2.legend(loc = 'best')\n",
    "\n",
    "# 添加标题和坐标说明\n",
    "ax2_2.set_title('Size of Sepal vs Size of Petal')\n",
    "ax2_2.set_xlabel('size of sepal')\n",
    "ax2_2.set_ylabel('size of petal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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REe3VtoBh+yrg4R5pl9leUmavBjZt1/4jIqJ/dbIN40jgly2WGbhM0kxJE3vL\nRNJESTMkzViwYEG/FzIiIiodCRiSjgeWAGe3WGVX2zsC+wJHS3pjq7xsn2q7y3bXqFGj2lDaiIiA\nDgQMSYcD+wOH2s3HrLQ9r7w/AFwA7DxwJYyIiGYGNGBI2gf4T+Cttp9qsc5aktbpngb2BmY1Wzci\nYkVJNV64z3VWBu28rXYq8Edga0lzJR0FnAKsA0wrt8x+r6w7RtIlZdPRwHRJNwLXAhfb/lW7yhkR\nKze7f14rg1XblbHtQ5okT2mx7jxgvzJ9N/CadpUrIiKWT570joiIWhIwIiKilgSMiIioJQEjIiJq\nScCIiIhaEjAiIqKWBIyIiKglASMiImpJwIiIiFoSMCIiopYEjIiIqCUBIyIiaknAiIiIWhIwIiKi\nlgSMiIiopW3jYcSy6XvELkONUb1WloFcImLgtfUKQ9Jpkh6QNKshbQNJ0yTdWd7Xb7Ht4WWdO8s4\n4MNaRv2KiMGu3VVSZwD79Ej7FHC57a2Ay8v880jaAPg88DpgZ+DzrQJLREQMjLYGDNtXAQ/3SD4A\nOLNMnwm8rcmmbwam2X7Y9iPANF4YeCIiYgB1og1jtO35ALbnS9qoyTpjgXsb5ueWtBeQNBGYCLD5\n5pv3c1Ej6umPNqhUKcZgN1jvkmr2r9X038n2qba7bHeNGjWqzcWKaC7tT7Ey6ETAuF/SJgDl/YEm\n68wFNmuY3xSYNwBli4iIFjoRMC4Euu96Ohz4eZN1LgX2lrR+aezeu6RFRESHtPu22qnAH4GtJc2V\ndBTwZeCfJd0J/HOZR1KXpB8C2H4Y+CJwXXmdWNIiIqJD5GFUedrV1eUZM2Z0uhgREUOGpJm2u+qs\nO1gbvSMiYpBJwIiIiFoSMCIiopYEjIiIqGVYNXpLWgDc0+lytMmGwIOdLkQstxy/oW04H78tbNd6\n6nlYBYzhTNKMuncyxOCT4ze05fhVUiUVERG1JGBEREQtCRhDx6mdLkCskBy/oS3Hj7RhRERETbnC\niIiIWhIwIiKilgSMQUrSEZLGdLocsfwknShpr+XYbndJF7WjTCsjSWMk/XQ5tvuhpG37WOdDkt67\n/KUbWtKGMUhJuhL4hO10vzuISRLV/9Ez/Zjn7lTHfv+a669qe0l/7X9lke9t2eUKYwBJWkvSxZJu\nlDRL0kGSXivpt5JmSrpU0iaSDgS6gLMl3SBpTUl7SvqTpJslnSZp9ZLnlyXdKukmSV8vaW+RdE1Z\n/9eSRnfycw8Fkr4i6d8b5k+QdKykT0q6rny/XyjLxkmaLem7wPXAZpLOKMf0Zkn/UdY7oxxLJO0k\n6Q/l2F8raR1Ja0g6vWzzJ0k0oz4PAAAGT0lEQVQTmpRrA0k/K/u/WtL2DeU7VdJlwFkD8BUNCb0c\nx1ll/ghJP5H0C+AySatI+q6kWyRdJOmShmN2paSuMv2EpEnl+F3d/T9V8v9EmX55+X+7UdL1kraU\ntLaky8v8zZIOGPAvpT/ZzmuAXsA7gR80zL8Y+AMwqswfBJxWpq8Eusr0GsC9wCvK/FnAMcAGwO08\nd6W4XnlfvyHt/cBJnf7sg/0F7AD8tmH+VuC9VLdTiurk6iLgjcA44Bng9WXd1wLTGrbtPg5nAAcC\nqwF3AzuV9HWBVYFjgdNL2iuBv5ZjvTtwUUmfDHy+TO8B3FCmTwBmAmt2+rsbTK8Wx/GNwKwyfwTV\nENAblPkDgUvK8d0YeAQ4sCxr/B808JYy/VXgMw3H4RNl+hrg7WV6DeBF5TivW9I2BO7q/t8ciq9V\niYF0M/B1SV+h+vF5BNgOmFbVbDACmN9ku62Bv9i+o8yfCRwNnAIsAn4o6eKSJ1RjoJ+rasz01YC/\ntOfjDB+2/yRpo9JuNIrq2GxPNTzwn8pqawNbUf2w32P76pJ+N/AySZOBi4HLemS/NTDf9nVlX48D\nSNqNKiBg+zZJ9wCv6LHtblQnGtj+jaSXSHpxWXah7adX/NMPHy2O4197rDbNz43guRvwE1dVin+T\ndEWLrP/Bc/9fM6lGC32WpHWAsbYvKOVYVNJHAv8t6Y1UJxljgdHA31bgY3ZMAsYAsn2HpNcC+wFf\nAqYBt9jepY9N1SK/JZJ2BvYEDgY+THUWOhk42faFpT78hP75BMPeT6nOODcGzqG6kviS7e83riRp\nHPBk97ztRyS9BngzVSB/N3Bk4yZUZ6g9NT2uNdbpzuvJJsvihcexp8bvrc4xAFjscpkALOWFv52t\n8jmUKnC91vZiSXOorj6GpLRhDKBy1vOU7f8Dvg68DhglaZeyfKSkV5XVFwLrlOnbgHGSXl7mDwN+\nK2lt4MW2L6Gqohpflr8YuK9MH97OzzTMnEMVeA+k+tG5FDiyfM9IGitpo54bSdoQWMX2ecBngR17\nrHIbMEbSTmX9dSStClxF9YOCpFcAm1NVMTZqXGd34MHuK5Roqedx7M104J2lLWM0VXXgMivHZK6k\ntwFIWl3Si6j+Fx8owWICsMXy5D9Y5ApjYL0a+JqkZ4DFwL8BS4Bvl2qGVYFvArdQ1X9/T9LTwC7A\n+4CflB+a64DvUbVh/FzSGlRnOP9R9nNCWfc+4GrgpQPy6YY427eUqoX7bM8H5kvaBvhjqTJ8AngP\n1Rlmo7HA6ZK6T8CO65HvPyQdBEyWtCbwNLAX8F2qY3wz1d/BEbb/XvbV7YSS903AU+QEoE89j2O5\nImzlPKor9FnAHVTtEI8t564PA74v6USq/+93AWcDv5A0A7iB6uRhyMpttRGxUpO0tu0nJL0EuBbY\n1faQbGNot1xhRMTK7iJJ61HdIPLFBIvWcoURERG1pNE7IiJqScCIiIhaEjAiIqKWBIyIXkg6vvQz\ndJOqfr1e1495X1IaWyOGhNwlFdFCeaByf2DH8nzEhlR30vQL2/v1V14RAyFXGBGtbUL1ZPXfAWw/\naHuepDmlV9Rry+vlAJJGSTpPVe+210nataSvred6pb1J0jtL+pwShJD0npLXDZK+L2lEeb2gF9yI\nTknAiGjtMqquy+8oXWC/qWHZ47Z3puoA8psl7VvAN2zvRNVh4A9L+meBx2y/2vb2wG8ad1KeJj+I\n6oGx8VRPkh9K1dXLWNvb2X41cHp7PmZEPamSimihPP37WuANwASqHoA/VRZPbXj/RpneC9i2oWuP\ndUsXFXtR9W3Une8jPXa1J1UX6deVbdcEHgB+Qe+94EYMqASMiF7YXko1LsKVpc+n7r6cGp947Z5e\nBdilZ5fjqqJAb0/ICjjT9nEvWNB7L7gRAypVUhEtSNpa0lYNSeOBe8r0QQ3vfyzTl1F1Md+9/fgW\n6ev32NXlwIHdPeGqGmVvixq94EYMqFxhRLS2NlUPs+tR9SZ7FzCR6s6p1SVdQ3XSdUhZ/6PAd0rP\nst3dl38I+K+SPouqfeILwPndO7F9q6TPUIYMperp9GiqXm1b9oIbMdDSl1TEMiqD4HTZfrDTZYkY\nSKmSioiIWnKFERERteQKIyIiaknAiIiIWhIwIiKilgSMiIioJQEjIiJq+f+DzO35sXOjegAAAABJ\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c3f8b9aa58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#不同种类的花瓣与萼片大小\n",
    "def boxplot(x_data, y_data, base_color, median_color, x_label, y_label, title):\n",
    "    _, ax = plt.subplots()\n",
    "    ax.boxplot(y_data\n",
    "               # 箱子是否颜色填充\n",
    "               , patch_artist = True\n",
    "               # 中位数线颜色\n",
    "               , medianprops = {'color': base_color}\n",
    "               # 箱子颜色设置，color：边框颜色，facecolor：填充颜色\n",
    "               , boxprops = {'color': base_color, 'facecolor': median_color}\n",
    "               # 猫须颜色whisker\n",
    "               , whiskerprops = {'color': median_color}\n",
    "               # 猫须界限颜色whisker cap\n",
    "               , capprops = {'color': base_color})\n",
    "    # 箱图与x_data保持一致\n",
    "    ax.set_xticklabels(x_data)\n",
    "    ax.set_ylabel(y_label)\n",
    "    ax.set_xlabel(x_label)\n",
    "    ax.set_title(title)\n",
    "\n",
    "\n",
    "bp_data=[data1['sepal_size'],data2['sepal_size'],data3['sepal_size']]\n",
    "\n",
    "# 调用绘图函数\n",
    "boxplot(x_data = species\n",
    "        , y_data = bp_data\n",
    "        , base_color = 'b'\n",
    "        , median_color = 'r'\n",
    "        , x_label = 'Species'\n",
    "        , y_label = 'Size of Sepal'\n",
    "        , title = 'Size Distribution of Sepal By Species')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jwFuAwW3TIuIb3WhvREQsze9fKmmbKm1OAiYBjB49uhtNmZlZLbo8WCzpXGAi8FlAwMeB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\n+/8EKS5qtWA8bQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c3f8b03828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bp_data=[data1['petal_size'],data2['petal_size'],data3['petal_size']]\n",
    "\n",
    "# 调用绘图函数\n",
    "boxplot(x_data = species\n",
    "        , y_data = bp_data\n",
    "        , base_color = 'b'\n",
    "        , median_color = 'r'\n",
    "        , x_label = 'Species'\n",
    "        , y_label = 'Size of Petal'\n",
    "        , title = 'Size Distribution of Petal By Species')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 练习3：餐厅小费情况分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_bill   tip     sex smoker  day    time  size\n",
       "0       16.99  1.01  Female     No  Sun  Dinner     2\n",
       "1       10.34  1.66    Male     No  Sun  Dinner     3\n",
       "2       21.01  3.50    Male     No  Sun  Dinner     3\n",
       "3       23.68  3.31    Male     No  Sun  Dinner     2\n",
       "4       24.59  3.61  Female     No  Sun  Dinner     4"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = sns.load_dataset(\"tips\")\n",
    "data.head()\n",
    "# 总消费，小费，性别，吸烟与否，就餐星期，就餐时间，就餐人数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'tip')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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p0giDxQLrz1jCqtcuCPx5K1tEtqo0NMims5ax6exlsTZwiZuGGx0usems+tds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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c3f9c286d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#小费与总消费的关系\n",
    "_, ax3_1 = plt.subplots()\n",
    "ax3_1.scatter(data['tip'],data['total_bill'])\n",
    "ax3_1.set_title('Tip vs Total bill')\n",
    "ax3_1.set_xlabel('total bill')\n",
    "ax3_1.set_ylabel('tip')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\ProgramData\\Anaconda3\\lib\\site-packages\\numpy\\core\\fromnumeric.py:57: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "  return getattr(obj, method)(*args, **kwds)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c3f9cec7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#男性与女性\n",
    "sex=data['sex'].unique()\n",
    "bp_data=[data[data['sex']==sex[0]]['tip'],data[data['sex']==sex[1]]['tip']]\n",
    "\n",
    "# 调用绘图函数\n",
    "boxplot(x_data = sex\n",
    "        , y_data = bp_data\n",
    "        , base_color = 'b'\n",
    "        , median_color = 'r'\n",
    "        , x_label = 'Sex'\n",
    "        , y_label = 'Tip'\n",
    "        , title = 'Distribution of Tip By Sex')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\ProgramData\\Anaconda3\\lib\\site-packages\\numpy\\core\\fromnumeric.py:57: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "  return getattr(obj, method)(*args, **kwds)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c3f9d10240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#抽烟与否\n",
    "smoker=data['smoker'].unique()\n",
    "bp_data=[data[data['smoker']==smoker[0]]['tip'],data[data['smoker']==smoker[1]]['tip']]\n",
    "\n",
    "# 调用绘图函数\n",
    "boxplot(x_data = smoker\n",
    "        , y_data = bp_data\n",
    "        , base_color = 'b'\n",
    "        , median_color = 'r'\n",
    "        , x_label = 'Smoke or Not'\n",
    "        , y_label = 'Tip'\n",
    "        , title = 'Distribution of Tip By Smoker')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\ProgramData\\Anaconda3\\lib\\site-packages\\numpy\\core\\fromnumeric.py:57: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "  return getattr(obj, method)(*args, **kwds)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c3f9db6a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#工作日与周末\n",
    "day=data['day'].unique()\n",
    "bp_data=[data[data['day'].isin(day[:2])]['tip'],data[data['day'].isin(day[2:4])]['tip']]\n",
    "\n",
    "# 调用绘图函数\n",
    "boxplot(x_data = ['weekend','weekday']\n",
    "        , y_data = bp_data\n",
    "        , base_color = 'b'\n",
    "        , median_color = 'r'\n",
    "        , x_label = 'Day'\n",
    "        , y_label = 'Tip'\n",
    "        , title = 'Distribution of Tip By Day')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\ProgramData\\Anaconda3\\lib\\site-packages\\numpy\\core\\fromnumeric.py:57: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "  return getattr(obj, method)(*args, **kwds)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c3f9dc27b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#午餐与晚餐\n",
    "time=data['time'].unique()\n",
    "bp_data=[data[data['time']==time[0]]['tip'],data[data['time']==time[1]]['tip']]\n",
    "\n",
    "# 调用绘图函数\n",
    "boxplot(x_data = time\n",
    "        , y_data = bp_data\n",
    "        , base_color = 'b'\n",
    "        , median_color = 'r'\n",
    "        , x_label = 'Time'\n",
    "        , y_label = 'Tip'\n",
    "        , title = 'Distribution of Tip By Time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\ProgramData\\Anaconda3\\lib\\site-packages\\numpy\\core\\fromnumeric.py:57: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "  return getattr(obj, method)(*args, **kwds)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c3f9dff9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#就餐人数\n",
    "size=data['size'].unique()\n",
    "bp_data=[]\n",
    "for i in range(len(size)):\n",
    "    bp_data.append(data[data['size']==size[i]]['tip'])\n",
    "\n",
    "# 调用绘图函数\n",
    "boxplot(x_data = size\n",
    "        , y_data = bp_data\n",
    "        , base_color = 'b'\n",
    "        , median_color = 'r'\n",
    "        , x_label = 'Size'\n",
    "        , y_label = 'Tip'\n",
    "        , title = 'Distribution of Tip By Size')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>smoker</th>\n",
       "      <th>Yes</th>\n",
       "      <th>No</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>3.051167</td>\n",
       "      <td>3.113402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>2.931515</td>\n",
       "      <td>2.773519</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "smoker       Yes        No\n",
       "sex                       \n",
       "Male    3.051167  3.113402\n",
       "Female  2.931515  2.773519"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#性别+抽烟\n",
    "tip_by_sex_smoke=data.groupby(['sex','smoker']).mean()['tip']\n",
    "tip_by_sex_smoke=tip_by_sex_smoke.unstack()\n",
    "tip_by_sex_smoke"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0,0,'Male'), Text(0,0,'Female')]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c3f9c15128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制分组柱状图的函数\n",
    "def groupedbarplot(x_data, y_data_list, y_data_names, colors, x_label, y_label,title):\n",
    "    _, ax = plt.subplots()\n",
    "    # 设置每一组柱状图的宽度\n",
    "    total_width = 0.8\n",
    "    # 设置每一个柱状图的宽度\n",
    "    ind_width = total_width / len(y_data_list)\n",
    "    # 计算每一个柱状图的中心偏移\n",
    "    alteration = np.arange(-total_width/2+ind_width/2, total_width/2+ind_width/2, ind_width)\n",
    "\n",
    "    # 分别绘制每一个柱状图\n",
    "    for i in range(0, len(y_data_list)):\n",
    "        # 横向散开绘制\n",
    "        ax.bar(x_data + alteration[i], y_data_list[i], color = colors[i], label = y_data_names[i], width = ind_width)\n",
    "    ax.set_ylabel(y_label)\n",
    "    ax.set_xlabel(x_label)\n",
    "    ax.set_title(title)\n",
    "    ax.legend(loc = 'upper right')\n",
    "\n",
    "\n",
    "\n",
    "# 调用绘图函数\n",
    "groupedbarplot(x_data = range(2)\n",
    "               , y_data_list = [tip_by_sex_smoke['Yes'],tip_by_sex_smoke['No']]\n",
    "               , y_data_names = ['Yes', 'No']\n",
    "               , colors = ['#539caf', '#7663b0']\n",
    "               , x_label = 'sex'\n",
    "               , y_label = 'tip'\n",
    "               ,title = 'Tip By Smoker and Sex')\n",
    "ax=plt.gca()\n",
    "ax.set_xticks(range(2))\n",
    "ax.set_xticklabels(tip_by_sex_smoke.index.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 练习4：泰坦尼克号海难幸存状况分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
       "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
       "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
       "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
       "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
       "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
       "\n",
       "     who  adult_male deck  embark_town alive  alone  \n",
       "0    man        True  NaN  Southampton    no  False  \n",
       "1  woman       False    C    Cherbourg   yes  False  \n",
       "2  woman       False  NaN  Southampton   yes   True  \n",
       "3  woman       False    C  Southampton   yes  False  \n",
       "4    man        True  NaN  Southampton    no   True  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = sns.load_dataset(\"titanic\")\n",
    "data.head()\n",
    "# 幸存与否，仓位等级，性别，年龄，堂兄弟姐妹数，父母子女数，票价，上船港口缩写，仓位等级，人员分类，是否成年男性，所在甲板，上船港口，是否幸存，是否单独乘船"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#不同仓位等级幸存比例\n",
    "# 绘制堆积柱状图\n",
    "def stackedbarplot(x_data, y_data_list, y_data_names, colors, x_label, y_label, title):\n",
    "    _, ax = plt.subplots()\n",
    "    # 循环绘制堆积柱状图\n",
    "    for i in range(0, len(y_data_list)):\n",
    "        if i == 0:\n",
    "            ax.bar(x_data, y_data_list[i], color = colors[i], align = 'center', label = y_data_names[i])\n",
    "        else:\n",
    "            # 采用堆积的方式，除了第一个分类，后面的分类都从前一个分类的柱状图接着画\n",
    "            # 用归一化保证最终累积结果为1\n",
    "            ax.bar(x_data, y_data_list[i], color = colors[i], bottom = y_data_list[i - 1], align = 'center', label = y_data_names[i])\n",
    "    ax.set_ylabel(y_label)\n",
    "    ax.set_xlabel(x_label)\n",
    "    ax.set_title(title)\n",
    "    ax.legend(loc = 'upper right') # 设定图例位置"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>survived</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pclass</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>80</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>97</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>372</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "survived    0    1\n",
       "pclass            \n",
       "1          80  136\n",
       "2          97   87\n",
       "3         372  119"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pclass_survived=data.groupby(['pclass','survived']).size().unstack()\n",
    "pclass_survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>survived</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>sum</th>\n",
       "      <th>yes_prop</th>\n",
       "      <th>no_prop</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pclass</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>80</td>\n",
       "      <td>136</td>\n",
       "      <td>216</td>\n",
       "      <td>0.629630</td>\n",
       "      <td>0.370370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>97</td>\n",
       "      <td>87</td>\n",
       "      <td>184</td>\n",
       "      <td>0.472826</td>\n",
       "      <td>0.527174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>372</td>\n",
       "      <td>119</td>\n",
       "      <td>491</td>\n",
       "      <td>0.242363</td>\n",
       "      <td>0.757637</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "survived    0    1  sum  yes_prop   no_prop\n",
       "pclass                                     \n",
       "1          80  136  216  0.629630  0.370370\n",
       "2          97   87  184  0.472826  0.527174\n",
       "3         372  119  491  0.242363  0.757637"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pclass_survived['sum']=pclass_survived[0]+pclass_survived[1]\n",
    "pclass_survived['yes_prop']=pclass_survived[1]/pclass_survived['sum']\n",
    "pclass_survived['no_prop']=pclass_survived[0]/pclass_survived['sum']\n",
    "pclass_survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0,0,'1'), Text(0,0,'2'), Text(0,0,'3')]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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rHoWZmS0TynlO4SFJ6wE9IuJfktoANZUPzQxe2T6qHYLZl0o5L8Q7CrgFuCoN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\nOScFMzPL/T/5P1B9cKNZ9wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c3f9cb3ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 调用绘图函数\n",
    "stackedbarplot(x_data = pclass_survived.index.values\n",
    "               , y_data_list = [pclass_survived['yes_prop'], pclass_survived['no_prop']]\n",
    "               , y_data_names = ['Survived', 'Not survived']\n",
    "               , colors = ['#539caf', '#7663b0']\n",
    "               , x_label = 'Pclass'\n",
    "               , y_label = 'Number of People'\n",
    "               , title = 'Number of People By Survived Or Not and Pclass')\n",
    "\n",
    "ax=plt.gca()\n",
    "ax.set_xticks(range(1,4))\n",
    "ax.set_xticklabels(pclass_survived.index.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>survived</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>81</td>\n",
       "      <td>233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>468</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "survived    0    1\n",
       "sex               \n",
       "female     81  233\n",
       "male      468  109"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#不同性别幸存比例\n",
    "sex_survived=data.groupby(['sex','survived']).size().unstack()\n",
    "sex_survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>survived</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>sum</th>\n",
       "      <th>yes_prop</th>\n",
       "      <th>no_prop</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>81</td>\n",
       "      <td>233</td>\n",
       "      <td>314</td>\n",
       "      <td>0.742038</td>\n",
       "      <td>0.257962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>468</td>\n",
       "      <td>109</td>\n",
       "      <td>577</td>\n",
       "      <td>0.188908</td>\n",
       "      <td>0.811092</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "survived    0    1  sum  yes_prop   no_prop\n",
       "sex                                        \n",
       "female     81  233  314  0.742038  0.257962\n",
       "male      468  109  577  0.188908  0.811092"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sex_survived['sum']=sex_survived[0]+sex_survived[1]\n",
    "sex_survived['yes_prop']=sex_survived[1]/sex_survived['sum']\n",
    "sex_survived['no_prop']=sex_survived[0]/sex_survived['sum']\n",
    "sex_survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0,0,'female'), Text(0,0,'male')]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZI1RrUpB0MHAT8CZJ09ldJf0oIv5R6uDMzKxhFfPy2lXAPhExHUDSJsBDgJOC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\nM7OMk4KZmWX+H3L9VW7uJiDnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c3f9e33160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 调用绘图函数\n",
    "stackedbarplot(x_data = [0,1]\n",
    "               , y_data_list = [sex_survived['yes_prop'], sex_survived['no_prop']]\n",
    "               , y_data_names = ['Survived', 'Not survived']\n",
    "               , colors = ['#539caf', '#7663b0']\n",
    "               , x_label = 'Sex'\n",
    "               , y_label = 'Number of People'\n",
    "               , title = 'Number of People By Survived Or Not and Sex')\n",
    "ax=plt.gca()\n",
    "ax.set_xticks(range(2))\n",
    "ax.set_xticklabels(sex_survived.index.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\ProgramData\\Anaconda3\\lib\\site-packages\\numpy\\core\\fromnumeric.py:57: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "  return getattr(obj, method)(*args, **kwds)\n"
     ]
    },
    {
     "data": {
      "image/png": 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6iD8mVoYkiNzrhA3haxD+3O6DT9ZnuSJ63s47rxHoYObMZrYAM2c2Ax2cd15j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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c3f9ddd400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#幸存or遇难の票价分布\n",
    "survived=data['survived'].unique()\n",
    "bp_data=[data[data['survived']==survived[0]]['fare'],data[data['survived']==survived[1]]['fare']]\n",
    "\n",
    "# 调用绘图函数\n",
    "boxplot(x_data = survived\n",
    "        , y_data = bp_data\n",
    "        , base_color = 'b'\n",
    "        , median_color = 'r'\n",
    "        , x_label = 'survived'\n",
    "        , y_label = 'fare'\n",
    "        , title = 'Distribution of Fare By Survived')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\ProgramData\\Anaconda3\\lib\\site-packages\\numpy\\core\\fromnumeric.py:57: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "  return getattr(obj, method)(*args, **kwds)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c3f9c7b9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#幸存or遇难の年龄分布\n",
    "data['age'].fillna(0,inplace=True)\n",
    "survived=data['survived'].unique()\n",
    "bp_data=[data[data['survived']==survived[0]]['age'],data[data['survived']==survived[1]]['age']]\n",
    "# 调用绘图函数\n",
    "boxplot(x_data=survived\n",
    "        , y_data = bp_data\n",
    "        , base_color = 'b'\n",
    "        , median_color = 'r'\n",
    "        , x_label = 'survived'\n",
    "        , y_label = 'age'\n",
    "        , title = 'Distribution of Age By Survived')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>pclass</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>embark_town</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Cherbourg</th>\n",
       "      <td>85</td>\n",
       "      <td>17</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Queenstown</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Southampton</th>\n",
       "      <td>127</td>\n",
       "      <td>164</td>\n",
       "      <td>353</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "pclass         1    2    3\n",
       "embark_town               \n",
       "Cherbourg     85   17   66\n",
       "Queenstown     2    3   72\n",
       "Southampton  127  164  353"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#不同上传港口の仓位等级\n",
    "embark_pclass=data.groupby(['embark_town','pclass']).size().unstack()\n",
    "#embark_pclass.fillna(0,inplace=True)\n",
    "embark_pclass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[embark_town\n",
       " Cherbourg       85\n",
       " Queenstown       2\n",
       " Southampton    127\n",
       " Name: 1, dtype: int64, embark_town\n",
       " Cherbourg       17\n",
       " Queenstown       3\n",
       " Southampton    164\n",
       " Name: 2, dtype: int64, embark_town\n",
       " Cherbourg       66\n",
       " Queenstown      72\n",
       " Southampton    353\n",
       " Name: 3, dtype: int64]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pclass_list=[embark_pclass.iloc[:,0],embark_pclass.iloc[:,1],embark_pclass.iloc[:,2]]\n",
    "pclass_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0,0,'Cherbourg'), Text(0,0,'Queenstown'), Text(0,0,'Southampton')]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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iLgYu7pxwaoekKRExotpxWPm8zroWr693q7WT1A3A4JL+euClKsViZrZOq7UE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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c3f9f109b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 调用绘图函数\n",
    "groupedbarplot(x_data = range(3)\n",
    "               , y_data_list = pclass_list\n",
    "               , y_data_names = embark_pclass.columns\n",
    "               , colors = ['#539caf', '#7663b0','#00ff00']\n",
    "               , x_label = 'embark_town'\n",
    "               , y_label = 'counts of pclass'\n",
    "               ,title = 'Counts of Pclass vs Embark Town')\n",
    "\n",
    "ax=plt.gca()\n",
    "ax.set_xticks(range(3))\n",
    "ax.set_xticklabels(embark_pclass.index.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\ProgramData\\Anaconda3\\lib\\site-packages\\numpy\\core\\fromnumeric.py:57: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "  return getattr(obj, method)(*args, **kwds)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c3f9bba780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#幸存or遇难の堂兄弟姐妹数量分布\n",
    "survived=data['survived'].unique()\n",
    "bp_data=[data[data['survived']==survived[0]]['sibsp'],data[data['survived']==survived[1]]['sibsp']]\n",
    "# 调用绘图函数\n",
    "boxplot(x_data=survived\n",
    "        , y_data = bp_data\n",
    "        , base_color = 'b'\n",
    "        , median_color = 'r'\n",
    "        , x_label = 'survived'\n",
    "        , y_label = 'sibsp'\n",
    "        , title = 'Distribution of Sibsp By Survived')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\ProgramData\\Anaconda3\\lib\\site-packages\\numpy\\core\\fromnumeric.py:57: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "  return getattr(obj, method)(*args, **kwds)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c3f883ac88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#幸存or遇难の父母子女数量分布\n",
    "survived=data['survived'].unique()\n",
    "bp_data=[data[data['survived']==survived[0]]['parch'],data[data['survived']==survived[1]]['parch']]\n",
    "# 调用绘图函数\n",
    "boxplot(x_data=survived\n",
    "        , y_data = bp_data\n",
    "        , base_color = 'b'\n",
    "        , median_color = 'r'\n",
    "        , x_label = 'survived'\n",
    "        , y_label = 'parch'\n",
    "        , title = 'Distribution of Parch By Survived')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>survived</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alone</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>175</td>\n",
       "      <td>179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>374</td>\n",
       "      <td>163</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "survived    0    1\n",
       "alone             \n",
       "False     175  179\n",
       "True      374  163"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#单独乘船 vs 幸存\n",
    "alone_survived=data.groupby(['alone','survived']).size().unstack()\n",
    "alone_survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x1c3fa0dfb70>,\n",
       "  <matplotlib.axis.XTick at 0x1c3fa0f0048>],\n",
       " <a list of 2 Text xticklabel objects>)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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mjUKeLqbC50IsJ5ki4+SiRGNmZo1GnquYivVcCDMza8TydDG1AL4LlBfWj4gr\niheWmZmVWp4upnuBRUAlBXdSm5nZxi1PgugYEccUPRIzM2tU8kzW97SkvYseiZmZNSp5WhCHAGek\nd1QvI3l+dETEPkWNzMzMSipPgji26FGYmVmjk+cy1/caIhAzM2tc8oxBrBNJoyXNk/RqQdk2kh6R\nNCP9uXVaLkkjJM2U9HK1u7fNzKwEipYggDFA9aufLgMejYiuwKPpMiTdWF3T12Dgz0WMy8zMciha\ngoiIJ4BPqhX3B6qeLzEWOKGg/OZIPAu0kdS+WLGZmVnditmCyLJ9RMwFSH9ul5Z3AGYV1Judlq1B\n0mBJUyRNmT9/flGDNTNryho6QdREGWVrPKQIICJGRkRFRFS0a9euyGGZmTVdDZ0gPqrqOkp/zkvL\nZwOdCup1BOY0cGxmZlagoRPEJOD09P3pJPM8VZUPSq9mOgBYVNUVZWZmpZHnRrl1Iuk2oDfQVtJs\n4BfANcBESWcB75M8vhTgQeA4YCbwOXBmseIyM7N8ipYgIuKUGlb1yagbwPnFisXMzNZeYxmkNjOz\nRsYJwszMMjlBmJlZJicIMzPL5ARhZmaZnCDMzCyTE4SZmWVygjAzs0xOEGZmlskJwszMMjlBmJlZ\nJicIMzPL5ARhZmaZnCDMzCyTE4SZmWVygjAzs0xOEGZmlskJwszMMjlBmJlZpqI9k7o2kt4FFgMr\ngOURUSFpG2ACUA68C5wcEZ+WIj4zMyttC+LwiOgeERXp8mXAoxHRFXg0XTYzsxJpTF1M/YGx6fux\nwAkljMXMrMkrVYIIYLKkSkmD07LtI2IuQPpzu6wNJQ2WNEXSlPnz5zdQuGZmTU9JxiCAgyNijqTt\ngEckvZF3w4gYCYwEqKioiGIFaGbW1JWkBRERc9Kf84C7gf2BjyS1B0h/zitFbGZmlmjwBCFpC0mt\nq94DRwGvApOA09NqpwP3NnRsZmb2tVJ0MW0P3C2p6vjjIuIhSS8AEyWdBbwPnFSC2MzMLNXgCSIi\n3ga6ZZR/DPRp6HjMzCxbY7rM1czMGhEnCDMzy+QEYWZmmZwgzMwskxOEmZllcoIwM7NMThBmZpbJ\nCcLMzDI5QZiZWSYnCDMzy+QEYWZmmZwgzMwskxOEmZllcoIwM7NMThBmZpbJCcLMzDI5QZiZWSYn\nCDMzy+QEYWZmmRpdgpB0jKQ3Jc2UdFmp4zEza6oaVYKQVAb8ETgW2AM4RdIepY3KzKxpalQJAtgf\nmBkRb0fEP4HxQP8Sx2Rm1iQ1K3UA1XQAZhUszwZ6FVaQNBgYnC4ukfRmA8W2cVOpA1ilLbCg1EGo\nEf1CLNV4/kk2hr/RznkqNbaBEz1aAAAEAklEQVQEkXXGsdpCxEhgZMOEYw1N0pSIqCh1HGY1aUp/\no42ti2k20KlguSMwp0SxmJk1aY0tQbwAdJXURdKmwEBgUoljMjNrkhpVF1NELJd0AfAwUAaMjojX\nShyWNSx3H1pj12T+RhURddcyM7Mmp7F1MZmZWSPhBGFmZpka1RiEbXwkrQBeKSg6ISLeraFuOXB/\nROxV/MjMviZpW+DRdPEbwApgfrq8f3rjbpPjBGHF9kVEdC91EGa1iYiPge4AkoYBSyJieGEdSSIZ\nt13Z8BGWhruYrMFJKpf0pKSp6eugjDp7Snpe0jRJL0vqmpZ/v6D8hnT+LrOikLSLpFcl/QWYCnSS\ntLBg/UBJ/52+317SXZKmpH+jB5Qq7vriBGHFtln6YT5N0t1p2Tzg2xHRAxgAjMjY7hzg+rT1UQHM\nlrR7Wv/gtHwF8L3in4I1cXsAoyJiX+CDWuqNAH6d3mV9MvDfDRFcMbmLyYotq4upOfAHSVUf8rtm\nbPcM8O+SOgJ3RcQMSX2A/YAXktY+m5EkG7NieisiXshR70hgt/RvE2BrSZtFxBfFC624nCCsFH4M\nfAR0I2nFflm9QkSMk/Qc8P+AhyX9K8lcXWMjYmhDBmtN3tKC9ytZfc64lgXvxUY2oO0uJiuFrYC5\n6WDfaSR3za9G0k7A2xExgmS6lX1IrjI5UdJ2aZ1tJOWaldKsPqR/s59K6ippE+A7Bav/Bzi/aiFt\nIW/QnCCsFP4EnC7pWZLupaUZdQYAr0qaBnwTuDkiXgf+A5gs6WXgEaB9A8VsVuVS4CGSLyyzC8rP\nBw5OL6p4HTi7FMHVJ0+1YWZmmdyCMDOzTE4QZmaWyQnCzMwyOUGYmVkmJwgzM8vkBGG2jiS9K6lt\nqeMwKxYnCDMzy+QEYZaDpHskVUp6TdLgjPU/SWf9fFXSRWlZuaTpkm5Mt5ssabN03c6SHkr3+aSk\nbzb0OZnVxTfKmeUgaZuI+CT9gH8B+BZQSTLTbGdgDHAAyXw8zwHfBz4FZgIVETFN0kRgUkTcIulR\n4Jx0EsJewK8i4ogGPzGzWniyPrN8LpRUNe9OJ6BrwbpDgLsjYimApLuAQ0nmkHonIqal9SqBckmt\ngIOA2wtm/mxR5PjN1poThFkdJPUmmcr5wIj4XNLjrDmLZ02WFbxfQTJF+SbAQj9pzxo7j0GY1W0r\n4NM0OXyTpCup0BPACZI2l7QFyQyfT9a0s4j4DHhH0kmQPMpSUrcixW62zpwgzOr2ENAsnUH2l8Cz\nhSsjYirJGMTzJOMP/x0RL9axz+8BZ0l6CXgN6F/fQZutLw9Sm5lZJrcgzMwskxOEmZllcoIwM7NM\nThBmZpbJCcLMzDI5QZiZWSYnCDMzy/T/Abs6DBxrFqKCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c3fa061a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, ax = plt.subplots()\n",
    "width=0.4\n",
    "index=alone_survived.index.values\n",
    "ax.bar(index, alone_survived[0], color = '#ff0000', label = 'Not survived', width = width)\n",
    "ax.bar(index+width, alone_survived[1], color = '#00ff00', label = 'Survived', width = width)\n",
    "\n",
    "ax.set_ylabel('numbers of People')\n",
    "ax.set_xlabel('alone')\n",
    "ax.set_title('People Survived vs Alone')\n",
    "ax.legend(loc = 'upper right')\n",
    "plt.xticks(index+width,index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
